CN106712111B - The multiple-energy-source economic load dispatching method of multi-target fuzzy optimal under active power distribution network environment - Google Patents
The multiple-energy-source economic load dispatching method of multi-target fuzzy optimal under active power distribution network environment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses the multiple-energy-source economic load dispatching methods of multi-target fuzzy optimal under active power distribution network environment, belong to the technical field of Automation of Electric Systems.The present invention proposes a kind of multi-Objective Fuzzy Optimization, for in the multiple-energy-source optimization process under active power distribution network environment the problem of multiple target multiple constraint, it is optimized based on Pareto theories, simultaneously, for the uncertain problem of intermittent energy in active power distribution network, fuzzy optimization mechanism is added in optimization process, obscures scheme collection to obtain best Pareto, reliable decision support is provided for dispatcher.
Description
Technical field
The invention discloses the multiple-energy-source economic load dispatching methods of multi-target fuzzy optimal under active power distribution network environment, especially suitable
For having in the active power distribution network environment of extensive fitful power access, belong to the technical field of Automation of Electric Systems.
Background technology
Since there are the accesses of extensive intermittent energy in active power distribution network environment so that the multipotency in active power distribution network
The features such as source optimization shows multiple target, multiple constraint and uncertainty are strong.Traditional optimization method can not optimize multiple simultaneously
Target is often only capable of providing single decision scheme, and enough decision supports can not be provided to decision dispatcher.Meanwhile tradition
Randomized optimization process excessively relies on probability density function when optimizing intermittent energy Economic Dispatch Problem, and its probability density letter
Number is also difficult to accurately obtain in practice in engineering, to be unable to get more reliable prioritization scheme.
Invention content
The goal of the invention of the present invention is the deficiency for above-mentioned background technology, provides multiple target under active power distribution network environment
Blurring mechanism is added to intermittent energy output in the multiple-energy-source economic load dispatching method of fuzzy optimization during multiple-objection optimization
Fuzzy processing is carried out, the fuzzy sides Pareto are obtained according to the intermittent energy generating optimization multi-objective Model after Fuzzy processing
Case collection realizes the blurring of each scheme schedules process, solves traditional optimization because intermittent energy output is uncertain
The technical issues of decision dispatcher can not being given to provide enough decision supports.
The present invention adopts the following technical scheme that for achieving the above object：
The multiple-energy-source economic load dispatching method of multi-target fuzzy optimal, includes the following steps under active power distribution network environment：
A, multiple-energy-source multiple target economical optimum model is established；
B, Fuzzy processing is carried out to the output conditional curve determined by each intermittent energy output predicted value and obtains each
The uncertain section that formula of the having a rest energy is contributed；
C, the charge and discharge of the output, energy storage device in the uncertain section and fired power generating unit contributed according to each intermittent energy
Electricity solves multiple-energy-source multiple target economical optimum model and obtains the fuzzy scheme collection of Pareto.
Further, under active power distribution network environment in the multiple-energy-source economic load dispatching method of multi-target fuzzy optimal, step A tools
Body is：For the electric system comprising fired power generating unit, wind-powered electricity generation, photovoltaic, electric vehicle, thermoelectricity pollution row minimum with cost of electricity-generating
High-volume minimum, each energy startup-shutdown number is at least target, considers that account load balancing constraints, spinning reserve constraint, each energy are contributed
Following multiple-energy-source multiple target economical optimum model is established in constraint, the constraint of fired power generating unit climbing rate, electric vehicle charge and discharge constraint：
Multiple target：
Account load balancing constraints：
Spinning reserve constrains：
Fired power generating unit units limits：Pci,min≤Pci,t≤Pci,max,
Fired power generating unit climbing rate constrains：DRci≤Pci,t-Pci,t-1≤URci,
Electric vehicle charge and discharge constrain：
Intermittent energy units limits：
Wherein, F1、F2、F3、F4Respectively thermoelectricity cost of electricity-generating calculates function, thermoelectricity discharge amount of pollution calculates function, each energy
Source start-stop time calculates function, electric vehicle charge and discharge cost-calculating function, and T is length dispatching cycle, NcFor fired power generating unit number
Amount, NrFor the quantity of intermittent energy, and Nr=Nw+Np, NwFor wind turbine quantity, NpFor photovoltaic quantity, ai、bi、ci、di、eiIt is i-th
The cost coefficient of a fired power generating unit, αi、βi、γi、ζi、λiFor the disposal of pollutants coefficient of i-th of fired power generating unit, Pci,t、Pci,t-1Point
Not Wei i-th of fired power generating unit in t moment, the output at t-1 moment, Prj,tIt is j-th of intermittent energy in the output of t moment, lit、
ljtRespectively fired power generating unit, intermittent energy are in the startup-shutdown number of t moment, lit-1、ljt-1Respectively fired power generating unit, intermittent
The energy is in the startup-shutdown number at t-1 moment, lit,ljt∈ { 0,1 }, lit-1,ljt-1∈ { 0,1 }, NBFor electric vehicle quantity, ∏d,t
For d-th of electric vehicle t moment cost coefficient,For d-th of electric vehicle t moment charge volume or discharge capacity,
PD,tFor in the workload demand of t moment, Ploss,tTo be lost in the power transmission of t moment, Respectively m-th of the energy, n-th of the energy
In the output of t moment, lmt、lntRespectively m-th of the energy, n-th of the energy are in the startup-shutdown number of t moment, Bmn、B0m、B00For
Network transmission impairment coefficient, Pci,max、Pci,minThe maximum output of respectively i-th fired power generating unit, minimum load, Pd,maxFor d
The maximum capacity of a electric vehicle, L contributes for spinning reserve accounts for the ratio degree of t moment workload demand, and L ∈ [0,100), DRci、
URciThe maximum climbing rate limitation of respectively i-th fired power generating unit, minimum climbing rate limitation,Indicate d-th of electric vehicle in t
Moment is in discharge condition,Indicate that d-th of electric vehicle is in charged state in t moment,For d-th of electric vehicle
In the maximum pd quantity of t moment,It is d-th of electric vehicle in the maximum charge amount of t moment, Vd,t-1、Vd,tRespectively d
A electric vehicle is in t-1 moment, the dump power of t moment, Vd,max、Vd,minThe respectively maximum of electric vehicle dump power, most
Small limitation, Pwqt、PpktRespectively q-th of wind turbine and k-th of photovoltaic are in the output of t moment, Pwqt、Respectively q-th of wind turbine
In the output minimum value and maximum value of t moment prediction, Ppkt、The output minimum value that respectively k-th of photovoltaic is predicted in t moment
And maximum value, q=1,2 ..., Nw, k=1,2 ..., Np。
Further, under active power distribution network environment in the multiple-energy-source economic load dispatching method of multi-target fuzzy optimal, step B
Specially：The predicted value contributed at each moment according to each intermittent energy determines what each intermittent energy was contributed at each moment
The forecast interval that each intermittent energy is contributed at each moment is averagely divided into nine equal portions, with each interval by forecast interval
The formula energy is output of each intermittent energy at each moment in the boundary curve of each each equal portions of moment output forecast interval
Journey curve, based on intermittent energy output prediction standard difference and 3- δ principles to each intermittent energy each moment output
Journey curve is blurred with each intermittent energy of determination in the uncertain section that each moment contributes.
Further, under active power distribution network environment in the multiple-energy-source economic load dispatching method of multi-target fuzzy optimal, step C
Specially：According to intermittent energy in the uncertain section that each moment contributes and fired power generating unit going out at each moment
Any individual of power, electric vehicle in the discharge and recharge initialization population at each moment is：It is poor using multiple target
Evolution algorithm is divided to be solved to obtain the fuzzy scheme collection of Pareto to multiple-energy-source multiple target economical optimum model, N indicates power train
The sum of all energy and electric vehicle, N=N in systemc+Nr+NB。
The side of advanced optimizing as the multiple-energy-source economic load dispatching method of multi-target fuzzy optimal under active power distribution network environment
Case, specific method multiple-energy-source multiple target economical optimum model solved using multiple target differential evolution algorithm in step C
For：
Mutation operation：It choosesIt is determined often for the ginseng of Evolution of Population process for mutation operator
Several bodies,Any two body during current Evolution of Population, X are indicated respectivelyGDuring current Evolution of Population
Optimum individual, γ are variation adjustment parameter, γ ∈ [0,1], UG+1For the parameter individual of next-generation Evolution of Population process；
Following selection operation is executed during every generation Evolution of Population：During according to current Evolution of Population
Optimum individual XGDetermining singular target value Fa(XG) and according to the candidate individual V for next-generation Evolution of PopulationG+1Determining list
A desired value Fa(VG+1) select to participate in the individual of next-generation Evolution of Population, current population is weighed using possibility degree p (α >=β)
Optimum individual X in evolutionary processGWith the candidate individual V for next-generation Evolution of PopulationG+1Quality, at p (α >=β)>It is selected when 0.5
Select optimum individual X during current Evolution of PopulationGNext-generation Evolution of Population is participated in, selection is for next at p (α >=β)≤0.5
For the candidate individual V of Evolution of PopulationG+1Next-generation Evolution of Population is participated in,α, β are respectively to determine optimum individual X during current Evolution of PopulationG
With the candidate individual V for next-generation Evolution of PopulationG+1Uncertain section, Δ α=αu-αl, Δ β=βu-βl, αu、αlRespectively
For the bound of uncertain section α, βu、βlThe respectively bound of uncertainty section β.
The present invention uses above-mentioned technical proposal, has the advantages that：The present invention proposes a kind of multi-target fuzzy optimal
Method, it is theoretical based on Pareto in the multiple-energy-source optimization process under active power distribution network environment the problem of multiple target multiple constraint
It is optimized, meanwhile, for the uncertain problem of intermittent energy in active power distribution network, mould is added in optimization process
Optimization Mechanism is pasted, scheme collection is obscured to obtain best Pareto, reliable decision support is provided for dispatcher.
Description of the drawings
Fig. 1 is that the present invention relates to the block diagrams of dispatching method.
Specific implementation mode
The technical solution of invention is described in detail with reference to Fig. 1.
(1) multiple-energy-source multiple target economic optimization scheduling model is established
Under active power distribution network environment, the large-scale distributed energy accesses so that multipotency source optimization shows multiple target, more
The features such as constraint need to fully consider each energy simultaneously with cost of electricity-generating, discharge amount of pollution and the minimum target of on-off times
Go out power limit, the constraint of climbing rate, account load balancing constraints, spinning reserve capacity and electric vehicle charge and discharge constraint etc., establishes more
Energy multiple target economic optimization scheduling model.
(1) optimization aim：
Thermoelectricity cost of electricity-generating：
Thermoelectricity discharge amount of pollution：
Each start-stop of generator set number：
Electric vehicle charge and discharge cost：
Wherein, F1、F2、F3、F4Respectively thermoelectricity cost of electricity-generating calculates function, thermoelectricity discharge amount of pollution calculates function, each energy
Source start-stop time calculates function, electric vehicle charge and discharge cost-calculating function, and T is length dispatching cycle, NcFor fired power generating unit number
Amount, NrFor the quantity of intermittent energy, and Nr=Nw+Np, NwFor the quantity of wind turbine, NpFor the quantity of photovoltaic, ai、bi、ci、di、ei
For the cost coefficient of i-th of fired power generating unit, αi、βi、γi、ζi、λiFor the disposal of pollutants coefficient of i-th of fired power generating unit, Pci,t、
Pci,t-1Respectively i-th of fired power generating unit is in t moment, the output at t-1 moment, Prj,tIt is j-th of intermittent energy in t moment
It contributes, lit、ljtRespectively fired power generating unit, intermittent energy are in the startup-shutdown number of t moment, lit,ljt∈ { 0,1 }, lit-1、
ljt-1Respectively fired power generating unit, intermittent energy are in the startup-shutdown number at t-1 moment, lit-1,ljt-1∈ { 0,1 }, NBFor electronic vapour
Vehicle quantity, ∏d,tFor d-th of electric vehicle t moment cost coefficient,For d-th of electric vehicle t moment charge volume
Or discharge capacity.
(3) constraints：
1. account load balancing constraints：
Wherein, PD,tFor in the workload demand of t moment, Ploss,tTo be lost in the power transmission of t moment, expression formula is： Respectively m-th of the energy, n-th of the energy
In the output of t moment, lmt、lntRespectively m-th of the energy, n-th of the energy are in the startup-shutdown number of t moment, Bmn、B0m、B00For
Network transmission impairment coefficient.
2. spinning reserve constrains：
Wherein, Pd,maxFor the maximum capacity of d-th of electric vehicle, Pci,maxFor i-th of fired power generating unit maximum output, L is rotation
Turn spare contribute and accounts for the ratio degree of t moment workload demand, L ∈ [0,100).
3. units limits：
Pci,min≤Pci,t≤Pci,max(7),
Wherein, Pci,minFor the minimum load of i-th of fired power generating unit.
4. output climbing rate constrains：
DRci≤Pci,t-Pci,t-1≤URci(8),
Wherein, DRci、URciThe minimum and maximum climbing rate limitation of respectively i-th fired power generating unit.
5. electric vehicle charge and discharge constrain：
Wherein,Indicate that d-th of electric vehicle is in electric discharge, charged state in t moment respectively,
Maximum pd quantity of respectively d-th of the electric vehicle in t moment, maximum charge amount, Vd,t-1、Vd,tRespectively d-th of electric vehicle
In t-1 moment, the dump power of t moment, Vd,min、Vd,maxThe respectively minimum of electric vehicle dump power, maximum limitation.
6. intermittent energy output forecast interval：
Pwqt、PpktRespectively q-th of wind turbine and k-th of photovoltaic t moment output,P wqt、Respectively q-th of wind turbine
T moment prediction output minimum value and maximum value,P pkt、The output minimum value that respectively k-th of photovoltaic is predicted in t moment
And maximum value, q=1,2 ..., Nw, k=1,2 ..., Np。
(2) the uncertain section that blurring intermittent energy output forecast interval is contributed with each intermittent energy of determination
It is nine equal portions by interval division, and will be each etc. according to the forecast interval that each intermittent energy is contributed in constraint 6.
Part boundary curve as wind turbine, photovoltaic t moment typical output conditional curveOn this basis, it is based on history
Wind-powered electricity generation and photovoltaic the prediction error of experience are estimated to obtain wind-powered electricity generation and the prediction standard difference of photovoltaic to be δwqt、δpkt.Based on 3- δ principles pair
Wind turbine, photovoltaic are blurred in the typical output conditional curve of t moment, can obtain wind turbine, photovoltaic is contributed not in t moment
Determination section is：
(3) above-mentioned multiple-energy-source Optimized model is solved using multi-Objective Fuzzy Optimization
First, the target of above-mentioned model is simplified, the sum of formula (1) and formula (4) are economic cost target：
F1'=F1+F4 (13)。
Then, multiple-energy-source Optimized model is optimized using multiple target differential evolution algorithm.Due to each intermittent energy
Output has been blurred, and all optimization solutions should also be blurring, and mould is added in original multiple target differential evolution algorithm below
Optimization Mechanism is pasted, it is specific as follows：
(1) population at individual initializes：According to basic units limits, several body is generated at random：
(2) according to the standard deviation δ in each individual generating processij, Fuzzy processing is carried out to individual and is obtained：
(3) population at individual is handled using differential evolution algorithm：
Mutation operator：
Wherein, γ ∈ [0,1] are variation adjustment parameter, UG+1For the parameter individual of next-generation Evolution of Population process.Intersect and calculates
Son still can then obtain the candidate individual V for next-generation Evolution of Population using original modeG+1。
Selection opertor：
Wherein, optimum individual X during more current Evolution of PopulationGWith the candidate individual for next-generation Evolution of Population
VG+1When, it is selected according to Pareto partial order selection mechanisms.In relatively singular target value Fa(XG) and Fa(VG+1) two interval numbers
When lower individual quality, a=1,2,3,4, the relationship between two individuals is described using possibility degree：
If α, β are respectively optimum individual and the candidate for next-generation Evolution of Population during determining current Evolution of Population
The uncertain section of body, it is assumed that Δ α=αu-αl, Δ β=βu-βl, αu、αlThe respectively bound of uncertainty section α, βu、
βlThe respectively bound of uncertainty section β, the comparison between α, β exist：
If then p (α >=β)>0.5, then α >=β, on the contrary then α<β.
(4) Pareto may finally be obtained according to above-mentioned optimization process and obscures scheme collection, scheme concentrates the tune of each scheme
It spends Cheng Jun to be blurred, there is stronger practicability during practical application, reliable decision branch is provided for dispatcher
It holds.
Claims (2)
1. the multiple-energy-source economic load dispatching method of multi-target fuzzy optimal under active power distribution network environment, which is characterized in that including as follows
Step：
A, multiple-energy-source multiple target economical optimum model is established；
B, the predicted value contributed at each moment according to each intermittent energy determines what each intermittent energy was contributed at each moment
The forecast interval that each intermittent energy is contributed at each moment is averagely divided into nine equal portions, with each interval by forecast interval
The formula energy is output of each intermittent energy at each moment in the boundary curve of each each equal portions of moment output forecast interval
Journey curve, based on intermittent energy output prediction standard difference and 3- δ principles to each intermittent energy each moment output
Journey curve is blurred with each intermittent energy of determination in the uncertain section that each moment contributes；
C, according to intermittent energy the uncertain section that each moment contributes and fired power generating unit each moment output,
Any individual of the electric vehicle in the discharge and recharge initialization population at each moment be：
Multiple-energy-source multiple target economical optimum model is solved using multiple target differential evolution algorithm to obtain the fuzzy sides Pareto
Case collection, N indicate the sum of all energy and electric vehicle in electric system, N=Nc+Nr+NB,
Use the specific method that multiple target differential evolution algorithm solves multiple-energy-source multiple target economical optimum model for：
Mutation operation：It choosesFor mutation operator, to determine, often the parameter for Evolution of Population process is a
Body,Any two body during current Evolution of Population, X are indicated respectivelyGIt is optimal during current Evolution of Population
Individual, γ are variation adjustment parameter, γ ∈ [0,1], UG+1For the parameter individual of next-generation Evolution of Population process；
Following selection operation is executed during every generation Evolution of Population：By comparing according to optimal during current Evolution of Population
Individual XGDetermining singular target value Fa(XG) and according to the candidate individual V for next-generation Evolution of PopulationG+1Determining single mesh
Scale value Fa(VG+1) select to participate in the individual of next-generation Evolution of Population, current Evolution of Population is weighed using possibility degree p (α >=β)
Optimum individual X in the processGWith the candidate individual V for next-generation Evolution of PopulationG+1Quality, selected in p (α >=β) ＞ 0.5
Optimum individual X during current Evolution of PopulationGNext-generation Evolution of Population is participated in, selection is for the next generation at p (α >=β)≤0.5
The candidate individual V of Evolution of PopulationG+1Next-generation Evolution of Population is participated in,
α, β are respectively to determine optimum individual X during current Evolution of PopulationGWith the candidate individual V for next-generation Evolution of PopulationG+1's
Uncertain section, Δ α=αu-αl, Δ β=βu-βl, αu、αlThe respectively bound of uncertainty section α, βu、βlRespectively
The bound of uncertain section β.
2. according to claim 1 under active power distribution network environment multi-target fuzzy optimal multiple-energy-source economic load dispatching method,
It is characterized in that, step A is specially：For the electric system comprising fired power generating unit, wind-powered electricity generation, photovoltaic, electric vehicle, with cost of electricity-generating
Minimum, thermoelectricity discharge amount of pollution minimum, each energy startup-shutdown number are at least target, consider account load balancing constraints, spinning reserve
Following multiple-energy-source multiple target is established in constraint, the constraint of each energy units limits, fired power generating unit climbing rate, electric vehicle charge and discharge constraint
Economical optimum model：
Multiple target：
Account load balancing constraints：
Spinning reserve constrains：
Fired power generating unit units limits：Pci,min≤Pci,t≤Pci,max,
Fired power generating unit climbing rate constrains：DRci≤Pci,t-Pci,t-1≤URci,
Electric vehicle charge and discharge constrain：
Intermittent energy units limits：
Wherein, F1、F2、F3、F4Respectively thermoelectricity cost of electricity-generating calculates function, thermoelectricity discharge amount of pollution calculates function, each energy opens
Stop number and calculate function, electric vehicle charge and discharge cost-calculating function, T is length dispatching cycle, NcFor fired power generating unit quantity, Nr
For the quantity of intermittent energy, and Nr=Nw+Np, NwFor wind turbine quantity, NpFor photovoltaic quantity, ai、bi、ci、di、eiFor i-th of fire
The cost coefficient of motor group, αi、βi、γi、ζi、λiFor the disposal of pollutants coefficient of i-th of fired power generating unit, Pci,t、Pci,t-1Respectively
I-th of fired power generating unit is in t moment, the output at t-1 moment, Prj,tIt is j-th of intermittent energy in the output of t moment, lit、ljtPoint
Not Wei fired power generating unit, intermittent energy in the startup-shutdown number of t moment, lit-1、ljt-1Respectively fired power generating unit, intermittent energy
In the startup-shutdown number at t-1 moment, lit,ljt∈ { 0,1 }, lit-1,ljt-1∈ { 0,1 }, NBFor electric vehicle quantity, Πd,tIt is
D electric vehicle t moment cost coefficient,It is d-th of electric vehicle in the charge volume or discharge capacity of t moment, PD,tFor
In the workload demand of t moment, Ploss,tTo be lost in the power transmission of t moment, Respectively m-th of the energy, n-th of energy
Source is in the output of t moment, lmt、lntRespectively m-th of the energy, n-th of the energy are in the startup-shutdown number of t moment, Bmn、B0m、B00
For network transmission impairment coefficient, Pci,max、Pci,minThe maximum output of respectively i-th fired power generating unit, minimum load, Pd,maxIt is
The maximum capacity of d electric vehicle, L contributes for spinning reserve accounts for the ratio degree of t moment workload demand, L ∈ [0,100),
DRci、URciThe maximum climbing rate limitation of respectively i-th fired power generating unit, minimum climbing rate limitation,Indicate d-th of electronic vapour
Vehicle is in discharge condition in t moment,Indicate that d-th of electric vehicle is in charged state in t moment,For d-th of electricity
Electrical automobile t moment maximum pd quantity,It is d-th of electric vehicle in the maximum charge amount of t moment, Vd,t-1、Vd,tPoint
Not Wei d-th of electric vehicle in t-1 moment, the dump power of t moment, Vd,max、Vd,minRespectively electric vehicle dump power
Maximum, minimum limitation, Pwqt、PpktRespectively q-th of wind turbine and k-th of photovoltaic t moment output,P wqt、Respectively q
The output minimum value and maximum value that a wind turbine is predicted in t moment,P pkt、The output that respectively k-th of photovoltaic is predicted in t moment
Minimum value and maximum value, q=1,2 ..., Nw, k=1,2 ..., Np。
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| CN107482690B (en) * | 2017-09-26 | 2020-08-11 | 广东工业大学 | Power system scheduling optimization method and system for cooperative scheduling of wind power and electric automobile |
| CN107968427B (en) * | 2017-11-03 | 2019-09-03 | 中国农业大学 | Coupling model construction method is unified in multiple-energy-source electric system based on equivalent source |
| CN108491976A (en) | 2018-03-29 | 2018-09-04 | 华南理工大学 | A kind of micro- energy net multiple target progress control method |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103326353A (en) * | 2013-05-21 | 2013-09-25 | 武汉大学 | Environmental economic power generation dispatching calculation method based on improved multi-objective particle swarm optimization algorithm |
| CN104009494A (en) * | 2014-04-16 | 2014-08-27 | 武汉大学 | Environmental economy power generation dispatching method |
| CN105528668A (en) * | 2015-08-04 | 2016-04-27 | 南通大学 | Dynamic environment and economy scheduling method of grid-connected wind power system |
| CN105631520A (en) * | 2015-07-16 | 2016-06-01 | 长沙理工大学 | Novel modeling method for extracting random and fuzzy uncertainty characteristics of wind speed |
| CN106257502A (en) * | 2016-07-20 | 2016-12-28 | 广东工业大学 | A kind of data processing method for the economic load dispatching containing wind energy turbine set and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8219258B1 (en) * | 2011-02-25 | 2012-07-10 | eCurv, Inc. | Queuing access to a shared power supply |
-
2017
- 2017-01-23 CN CN201710058300.9A patent/CN106712111B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103326353A (en) * | 2013-05-21 | 2013-09-25 | 武汉大学 | Environmental economic power generation dispatching calculation method based on improved multi-objective particle swarm optimization algorithm |
| CN104009494A (en) * | 2014-04-16 | 2014-08-27 | 武汉大学 | Environmental economy power generation dispatching method |
| CN105631520A (en) * | 2015-07-16 | 2016-06-01 | 长沙理工大学 | Novel modeling method for extracting random and fuzzy uncertainty characteristics of wind speed |
| CN105528668A (en) * | 2015-08-04 | 2016-04-27 | 南通大学 | Dynamic environment and economy scheduling method of grid-connected wind power system |
| CN106257502A (en) * | 2016-07-20 | 2016-12-28 | 广东工业大学 | A kind of data processing method for the economic load dispatching containing wind energy turbine set and device |
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